Name
Abstract | ||
|---|---|---|
We propose an unsupervised method for detecting spam documents from a given set of documents, based on equivalence relations on strings. We give three measures for quantifying the alienness (i.e. how different they are from others) of substrings within the documents. A document is then classified as spam if it contains a substring that is in an equivalence class with a high degree of alienness. The proposed method is unsupervised, language independent, and scalable. Computational experiments conducted on data collected from Japanese web forums show that the method successfully discovers spams. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-75488-6_16 | Discovery Science |
Keywords | Field | DocType |
high degree,equivalence class,equivalence relation,unsupervised spam detection,unsupervised method,japanese web forum,computational experiment,string alienness measure,spam document,web pages,col,computer experiment | Data mining,Substring,Equivalence relation,Computer science,Artificial intelligence,Equivalence class,Machine learning,Scalability | Conference |
Volume | ISSN | ISBN |
4755 | 0302-9743 | 3-540-75487-3 |
Citations | PageRank | References |
10 | 0.61 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuyuki Narisawa | 1 | 33 | 6.82 |
| Hideo Bannai | 2 | 620 | 79.87 |
| Hatano, Kohei | 3 | 88 | 21.16 |
| Masayuki Takeda | 4 | 902 | 79.24 |